Further Notes on Our Recent Research on AI Delegation and Long-Horizon Reliability
Our recent paper, “LLMs Corrupt Your Documents When You Delegate”, has generated discussion about the reliability of AI systems in delegated workflows. We appreciate the interest in this work and want to clarify several important points about what the paper does—and does not—claim.
The research aims to develop robust evaluation methods for long-horizon delegated and collaborative tasks. More broadly, this work reflects an ongoing effort to better understand the gap between strong benchmark performance and certain real-world tasks. Using a controlled evaluation methodology, we examine how well information is preserved across these extended workflows. Within this constrained setting, we observe that models can accumulate fidelity degradation over repeated edits. Note however, that current production systems can mitigate these effects through verification loops, orchestration, and domain-specific tooling.